Perceptual image hashing using center-symmetric local binary patterns

Davarzani, Reza; Mozaffari, Saeed; Yaghmaie, Khashayar

doi:10.1007/s11042-015-2496-6

Cited by 81 publications

(75 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method does not evaluate the robustness of the algorithm under image rotation. Davarzani et al [36] use CSLBP to extract features from each nonoverlapping block and generate a hash value by inner products of feature vectors. The identification accuracy of this method will decrease when facing geometric manipulations.…”

Section: Related Workmentioning

confidence: 99%

“…In these algorithms, the R&A SCH hashing [10] and the SIFT-QZMs hashing [25] are based on SIFT feature extraction. The SVD-CSLBP hashing [36] uses LBP to extract the texture features of an image. The GF-LVQ hashing [37] and the RP-NMF hashing [38] have considered the robustness of hashing algorithms under combined attacks using rotation with other content-preserving operations, while other combinations of attacks are not evaluated in their experiments.…”

Section: Perceptual Robustnessmentioning

confidence: 99%

“…Manipulation R&A SCH hashing [10] SIFT-QZMs hashing [25] SVD-CSLBP hashing [36] GF-LVQ hashing [37] RP-NMF hashing [38] Proposed hashing…”

Section: Perceptual Robustnessmentioning

confidence: 99%

“…R&A SCH hashing [10] SIFT-QZMs hashing [25] SVD-CSLBP hashing [36] GF-LVQ hashing [37] RP-NMF hashing [38] Proposed hashing R&A SCH hashing [10] SIFT-QZMs hashing [25] SVD-CSLBP hashing [36] GF-LVQ hashing [37] RP-NMF hashing [38] Proposed hashing 0.432 and = 0.0427, respectively. Given a threshold , the collision probability of the proposed image hashing scheme can be calculated by Table 5 presents the collision probabilities of the proposed image hashing scheme under different thresholds .…”

mentioning

confidence: 99%

See 3 more Smart Citations

SSL: A Novel Image Hashing Technique Using SIFT Keypoints with Saliency Detection and LBP Feature Extraction against Combinatorial Manipulations

Xue

Yuan

Liu

et al. 2019

Security and Communication Networks

View full text Add to dashboard Cite

Image hashing schemes have been widely used in content authentication, image retrieval, and digital forensic. In this paper, a novel image hashing algorithm (SSL) by incorporating the most stable keypoints and local region features is proposed, which is robust against various content-preserving manipulations, even multiple combinatorial manipulations. The proposed algorithm combines S_cale invariant feature transform (SIFT) with S_aliency detection to extract the most stable keypoints. Then, the L_ocal binary pattern (LBP) feature extraction method is exploited to generate local region features based on these keypoints. After that, the information of keypoints and local region features are merged into a hash vector. Finally, a secret key is used to randomize the hash vector, which can prevent attackers from forging the image and the hash value. Experimental results demonstrate that the proposed hashing algorithm can identify visually similar images which are under both single and combinatorial content-preserving manipulations, even multiple combinations of manipulations. It can also identify maliciously forged images which are under various content-changing manipulations. The collision probability between hashes of different images is nearly zero. Besides, the evaluation of key-dependent security shows that the proposed scheme is secure that an attacker cannot forge or estimate the correct hash value without the knowledge of the secret key.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Perceptual Robustnessmentioning

confidence: 99%

“…Manipulation R&A SCH hashing [10] SIFT-QZMs hashing [25] SVD-CSLBP hashing [36] GF-LVQ hashing [37] RP-NMF hashing [38] Proposed hashing…”

Section: Perceptual Robustnessmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

SSL: A Novel Image Hashing Technique Using SIFT Keypoints with Saliency Detection and LBP Feature Extraction against Combinatorial Manipulations

Xue

Yuan

Liu

et al. 2019

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…The first step of FRMCD is to divide the face image to extract CS-LBP texture features. If the number of subblocks is too few, the information of local features extracted by CS-LBP is too little to recognize the face images accurately [29,30]. If the number of subblocks is excessive, then a large feature information extracted by CS-LBP makes the training samples input DBN network too sparse and the classifier performance bad and the recognition rate will decrease [31,32].…”

Section: Experimental Study On Different Partitioningmentioning

confidence: 99%

The Face Recognition Method Based on CS-LBP and DBN

Sun

Yin

Yang

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

At present, the face recognition method based on deep belief network (DBN) has advantages of automatically learning the abstract information of face images and being affected slightly by active factors, so it becomes the main method in the face recognition area. Because DBN ignores the local information of face images, the face recognition rate based on DBN is badly affected. To solve this problem, a face recognition method based on center-symmetric local binary pattern (CS-LBP) and DBN (FRMCD) is proposed in this paper. Firstly, the face image is divided into several subblocks. Secondly, CS-LBP is used to extract texture features of each image subblock. Thirdly, texture feature histograms are formed and input into the DBN visual layer. Finally, face classification and face recognition are completed through deep learning in DBN. Through the experiments on face databases ORL, Extend Yale B, and CMU-PIE by the proposed method (FRMCD), the best partitioning way of the face image and the hidden unit number of the DBN hidden layer are obtained. Then, comparative experiments between the FRMCD and traditional methods are performed. The results show that the recognition rate of FRMCD is superior to those of traditional methods; the highest recognition rate is up to 98.82%. When the number of training samples is less, the FRMCD has more significant advantages. Compared with the method based on local binary pattern (LBP) and DBN, the time-consuming of FRMCD is shorter.

show abstract